In the coordinated lot-size problem, a major setup cost is incurred when at least one member of a product family is produced and a minor setup cost for each different item produced. This research consolidates the various modeling and algorithmic approaches reported in the literature for the coordinated replenishment problem with deterministic dynamic demand. For the two most effective approaches, we conducted extensive computational experiments investigating the quality of the lower bound associated with the model’s linear programming relaxation and the computational efficiency of the algorithmic approaches when used to find heuristic and optimal solutions. Our findings indicate the superiority of the plant location type problem formulation over the traditional approach that views the problem as multiple single-item Wagner and Whitin problems that are coupled by major setup costs. Broader implications of the research suggest that other classes of deterministic dynamic demand lot-size problems may also be more effectively modeled and solved by adapting plant location type models and algorithms.